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Minimum Detectable Effects

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  • Howard S. Bloom

    (New York University)

Abstract

This article describes a simple way to assess the statistical power of experimental designs. The approach presented is based on the concept of a minimum detectable effect, which, intuitively, is the smallest true impact that an experiment has a good chance of detecting. The article illustrates how to compute minimum detectable effects and how to apply this concept to the assessment of alternative experimental designs. Applications to impact estimators for both continuous and binary outcome measures are considered

Suggested Citation

  • Howard S. Bloom, 1995. "Minimum Detectable Effects," Evaluation Review, , vol. 19(5), pages 547-556, October.
    • Handle: RePEc:sae:evarev:v:19:y:1995:i:5:p:547-556
    DOI: 10.1177/0193841X9501900504
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    References listed on IDEAS

    as
    1. Woodbury, Stephen A & Spiegelman, Robert G, 1987. "Bonuses to Workers and Employers to Reduce Unemployment: Randomized Trials in Illinois," American Economic Review, American Economic Association, vol. 77(4), pages 513-530, September.
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    Cited by:

    1. Sarah Baird & Aislinn Bohren & Craig McIntosh & Berk Ozler, 2014. "Designing Experiments to Measure Spillover Effects," PIER Working Paper Archive 14-032, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. Blackman, Allen & Goff, Leonard & Rivera Planter, Marisol, 2018. "Does eco-certification stem tropical deforestation? Forest Stewardship Council certification in Mexico," Journal of Environmental Economics and Management, Elsevier, vol. 89(C), pages 306-333.
    3. Eric W. Djimeu & Deo-Gracias Houndolo, 2016. "Power calculation for causal inference in social science: sample size and minimum detectable effect determination," Journal of Development Effectiveness, Taylor & Francis Journals, vol. 8(4), pages 508-527, October.

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